CN103270801B - Method of and system for locating the position of user equipment - Google Patents

Abstract

The present invention provides a method and system for locating a position of user equipment in a telecommunication system comprising at least three base stations. The method comprising the step of: performing a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm; constructing initially estimated positions based on a result of the first position estimation/and determining a finally estimated position of the user equipment. The system comprises a first position estimation module adapted to perform a first position estimation for the user equipment using a Quadratic Correction Least Square algorithm and to construct initially estimated positions of the user equipment based on a result of the first position estimation; and a determination module connected to the first position estimation module and adapted to determine a finally estimated position of the user equipment. The method and system provide a better viability in real network, especially in case of poor UE/BS geometry and poor TDOA measurements and gives better robustness and accuracy compare to the legacy solution.

Description

Method and system for locating the position of a user equipment

Technical Field

The present invention relates to the field of telecommunications networks. In particular, the present invention relates to a method and system for locating the position of a user equipment in a cellular telecommunications network.

Background

Wireless mobile communication devices continue to shape our world. One of the benefits of using some mobile devices is that the location of the mobile device, and thus the user, can be determined or otherwise estimated. For example, locating those people who the user seeks to respond to an emergency during the emergency would be a significant benefit.

Accordingly, mobile device positioning techniques have been implemented to determine or otherwise estimate a location of a mobile device.

As is well known to those skilled in the art, time of arrival (TOA) and time difference of arrival (TDOA) are widely used techniques for geolocation applications. Both of these methods play important roles in satellite-based systems such as the Global Positioning System (GPS) and Galileo (Galileo), and ground radio-based systems such as long-range navigation (LORAN), as well as other wireless positioning systems. TOA-based positioning techniques are commonly referred to as circles or spheres for 2D or 3D locations, respectively, while those based on TDOA are referred to as hyperboloids or hyperboloids.

Furthermore, with the development of location technologies, observed time difference of arrival (OTDOA) has become one of the main location technologies in modern cellular telecommunication networks. The position of the UE can be calculated based on the following measured parameters:

1) TDOA measurements of downlink radio signals;

2) actual Relative Time Difference (RTD) between transmissions of Base Stations (BS) when TDOA measurements are made; and

3) the geographical position of the BS whose signal is measured.

Fig. 1 shows a case where the OTDOA technique is employed. In such a case, as shown in fig. 1, at least three BSs 101, 102 and 103 need to be involved to locate the position of the UE 100, because the position of the UE can be determined by the intersection of at least two hyperbolas as shown in fig. 1. More TDOA measurements lead to better accuracy.

The accuracy of each of these measurements thus affects the overall accuracy of the position estimate. There are several schemes for determining the RTD. One solution is to synchronize the transmissions of the BSs. In this case, the RTD is a known constant value that can be entered in the database and used by the calculation function when making the orientation estimate. Synchronization should achieve an accuracy level on the order of tens of nanoseconds, since a 10 nanosecond uncertainty affects 3 meters of error in the position estimate. Drift and jitter in the synchronization timing should also be well controlled because they also affect the uncertainty in the bearing estimate. Synchronization to this level of accuracy is currently only easily provided by satellite-based time transfer techniques. Typically in TDD mode of operation, the BSs are synchronized.

Alternatively, the BS may remain free-running within some constraint of maximum frequency error. In this case, the RTD will change over time, in which case it changes slowly. The rate of change will depend on the frequency difference and jitter between the BSs.

The OTDOA method can operate in two modes: UE assisted OTDOA and UE based OTDOA. The two modes differ in the actual orientation calculation performed. In the UE-assisted mode, the UE measures TDOAs of several cells and transmits the measurement results to the network, wherein the location server performs position calculation.

In the UE-based mode, the UE makes measurements and also performs position calculations and therefore requires additional information. For example, the UE may require measured positions of BSs and timing relationships between them for position calculation in the UE-based mode.

OTDOA has been standardized by 3GPP GERAN, which is referred to therein as E-OTD, but has not been deployed in an actual network.

In E-UTRAN, reference signals for OTDOA have been considered in physical layer signal design, which makes OTDOA even more promising. Some american operators have initiated planning for LTE-OTDOA deployment in 2010-2011. In addition, it is also clear that the OTDOA-related protocol in E-UTRAN will be directly adopted by other standardization bodies such as OMA for user plane location. Therefore, OTDOA is highly likely to ramp up in near future use.

OTDOA positioning essentially belongs to hyperbolic positions because the measurements are a set of time differences corresponding to a set of hyperbolas. Extensive research has been conducted on related solutions/algorithms, of which two approaches are most widely adopted and discussed:

the method comprises the following steps: the combination of Taylor series linearization and iterative Weighted Least Squares (WLS) algorithm was originally proposed by the "Taylor series estimated Position-location solution" (Position-location solutions by Taylor-series estimation, IEEE trans. aerosp. electron. syst., vol. AES-12, pp. 187 □, mar.1976), written by w.h. Foy, and is hereinafter referred to as Taylor method.

The method 2 comprises the following steps: quadratic Correction Least Squares (QCLS), also known as Chan algorithm proposed in "simple and effective estimator for hyperbolical location" ("a simple and effective estimator for hyperbolical location," IEEE trans. signal Processing, vol. 42, pp. 1905 351915, aug.1994). This method is hereinafter referred to as the Chan method.

Also combinations of the two methods, e.g. feeding the results of method 2 into method 1, can be found in publications like the modified Taylor algorithm in TDOA subscriber location (an improved Taylor algorithm in TDOA asset location, Communication Technology Proceedings, 2003.ICCT 2003 Volume 2, Issue, 9-11 April 2003 page(s): 981. 984 vol.2).

The taylor method can provide a good, stable estimate even in cases where TDOA measurements are poorly accurate, e.g., due to low signal-to-noise ratio (SNR). It is an iterative approach and therefore requires an initial guess input as a starting point. Sometimes, it is necessary to approach the true starting point to avoid local minima.

The Chan method is in a closed form and computationally efficient, it can approximate the Cramer-Rao lower bound (CRLB) in case of high SNR, but with lower accuracy than the taylor method in case of low SNR. Furthermore, this approach requires additional algorithm branches when linear arrays are used.

Since both methods have both advantages and disadvantages, their proper combination has attracted some research. The output of the Chan method can be used as an initial guess input to the taylor method in order to achieve better accuracy and robustness.

The Chan method generally provides a good estimate. This also matches the simulation results. However, the noise level, which is simulated for example in terms of range, is usually very low, i.e. at 10^-3To 10^-5And the distance between BSs is about 4 to 5. These numbers are unitless, but it can be found that the Root Mean Square (RMS) error is only 0.06% to 0.8% of the BS distance. For an average BS distance of 1 km in an actual network, the corresponding measurement error is therefore 0.6-8 meters, which is too optimistic to justify the feasibility of the algorithm. In fact, the reason for using such a small error may be to prove that it can approach the CRLB in high SNR situations.

If the noise variance increases to 0.03, the RMS error becomes approximately 45 meters, which for embodiments of the invention is considered reasonable taking into account multipath and non-ideal timing effects. The simulation results of a reasonable simple geometry due to the fact that in a real network a UE cannot typically listen to many BSs are shown in fig. 2, with the following conditions:

TDOA measurement error: for each measurement, a range of independent identically distributed Additive White Gaussian Noise (AWGN) was converted. Variance = 0.03

The operation times are as follows: 5000

The true UE location 211 is [10, 6 ]. In fig. 2, open dots 201, 203, 205 and 207 respectively represent the location of the BS azimuth, and a solid dot 211 represents the terminal actual azimuth, and a dark area 209 is the Chan method result.

It should be noted that the coordinates and origin are chosen for ease of evaluation. Applicants' simulations demonstrate that generality is not lost.

It can be seen that the Chan method can also provide a good estimate in this case. However, for some "non-peripheral" topology, the Chan method cannot even provide a close estimate. An example is shown in fig. 3, which fig. 3 shows the same BS topology and simulation conditions as described above, but with different UE orientations.

As shown in fig. 3, points 301, 303, 305, and 307 represent the locations of the base stations. The areas 309 and 311 are estimated orientations according to the Chan method, while the actual orientation of the user equipment is at point 313.

Unfortunately, such "non-ambient" situations as described above are not negligible, since such non-ideal environments, such as mountainous areas, building shelters, are highly likely to exist in actual network deployments. Additionally, for cascaded solutions, i.e., a combination of Chan and Taylor methods, simulations show that initial guess input of such differences will in most cases diverge the Taylor method or erroneously converge to a local minimum. The final position estimate will therefore usually not be available or with a large error, and the OTDOA will thus fail, e.g. because it cannot pass a mental health check (sanity check) depending on the cell-based positioning results.

Simulations show that the Chan method produces unreasonable results for situations where the SNR is very low. Fig. 3 is an example of such a scenario.

Disclosure of Invention

It is therefore an object of embodiments of the present invention to address the above disadvantages by providing a method and system for locating the position of a user equipment in a telecommunications environment.

According to a first embodiment of the present invention, this object is achieved by a method for locating or determining the position of a user equipment in a telecommunication system comprising at least three base stations, the method comprising the steps of: performing a first orientation estimation for the user equipment using a quadratic correction least squares algorithm; constructing an initial estimated position based on the result of the first position estimation; and determining a final estimated position of the user equipment.

According to a further embodiment, the step of determining comprises calculating a weighted difference for the initial estimated orientation; and selecting at least one of the initially estimated orientations having the smallest weighted difference as an initial guess for performing a second orientation estimation using a combination of taylor series linearization and an iterative weighted least squares algorithm; or all of the initially estimated orientations are applied to perform the second orientation estimation using a combination of taylor series linearization and an iterative weighted least squares algorithm.

According to another embodiment, the step of selecting at least one of the initially estimated positions further comprises: if the difference between the second least weighted difference and the least weighted difference is greater than a threshold, or if the quotient of the second least weighted difference divided by the least weighted difference is greater than another threshold, then the initial estimated bearing corresponding to the least weighted difference is selected, otherwise both the initial estimated bearing corresponding to the least weighted difference and the initial estimated bearing corresponding to the second least weighted difference are selected.

According to yet another embodiment, the step of determining further comprises: if one of the initially estimated bearings having the smallest weighted difference is selected as the initial guess bearing in order to perform the second bearing estimation, calculating an additional weighted difference for an additional estimated bearing calculated from the second bearing estimation, and determining the bearing having the smallest weighted difference as the last estimated bearing from a group including the previously selected bearing from the initially estimated bearings and the additional estimated bearing; if more than one initially estimated position is selected as the initially guessed position for performing the second position estimate, an additional weighted difference is calculated for additional estimated positions calculated from the second position estimate, and the position having the smallest weighted difference is determined as the last estimated position from the group comprising the previously selected position from the initially estimated positions and the additional estimated positions.

According to the above embodiment, the first orientation estimation is performed by employing the following equation:

where z is the estimated quantity of the signal,

，

，

，

and is and

wherein,is the covariance matrix of the TDOA measurements,is for vectorsM is the number of base stations measured, c is the speed of light,andthe location of the ith base station and terminal, r_iIs the distance between the ith base station and the user equipment, an、、Respectively represent、、I =1 to M.

The first orientation estimation may also be performed by employing the following equation:

wherein z is₁Is a further estimator of the amount of time,

，

，

and is and

，

whereinIs obtained by usingAlternative vectorIn (1)An intermediate matrix that can be obtained, andis thatPreliminary estimation of (1).

The initial estimated position may be constructed by:

；

；

(ii) a And is

，

Wherein,indicating an initial estimated orientation, anDetermined using an equation of the form:

if it is notThen, then；

If it is not，Wherein z is₁(j) Is the estimated quantity z₁The vector elements of (2).

The step of calculating the weighted difference may be performed by using an equation of the form:

，

or

Wherein:

is a TDOA measurement representing the time difference between the ith base station and the first base station;

c is the speed of light;

is the ith diagonal element in the covariance matrix of the TDOA measurements; and

abs (.) is an operator that computes absolute values; and

is the distance between the kth initial estimated position of the user equipment and the ith base station.

According to a further embodiment of the invention, the step of determining further comprises: calculating a weighted difference for the initial estimated orientation; and determining one of the initially estimated positions having the smallest weighted difference as a last estimated position.

Embodiments of the present invention also provide a positioning system adapted for positioning or determining the position of a user equipment in a telecommunication system comprising at least three base stations, the system comprising: a first orientation estimation module adapted to perform a first orientation estimation for the user equipment using a quadratic correction least squares algorithm and to construct an initial estimated orientation of the user equipment based on the result of the first orientation estimation; and a determining module connected to the first orientation estimating module and adapted to determine a last estimated orientation of the user equipment. The determination module further comprises: a second position estimation module adapted to perform a second position estimation using a combination of taylor series linearization and an iterative weighted least squares algorithm and to calculate or construct one or more further estimated positions based on the results of the second position estimation; a calculation module connected to the first and second position estimation modules and adapted to calculate a weighted difference for the initial estimated position and a further weighted difference for one or more further estimated positions; and a determination unit connected to the calculation module, the determination unit configured to determine the position having the smallest weighted difference from a group comprising the previously selected position from the initially estimated position and the further estimated position as the last estimated position. The determination module may further comprise a selection module connected to the calculation module and the second position estimation module, the selection module being adapted to select at least one of the initially estimated positions having the smallest weighted difference as the initially guessed position for performing the second position estimation. The selection module may be configured to select the initial estimated position corresponding to the smallest weighted difference if the difference between the second smallest weighted difference and the smallest weighted difference is greater than a threshold value, or if the quotient of the second smallest weighted difference divided by the smallest weighted difference is greater than another threshold value, and otherwise select both the initial estimated position corresponding to the smallest weighted difference and the initial estimated position corresponding to the second smallest weighted difference.

Embodiments of the present invention also provide a computer-readable medium comprising logic for locating or determining an orientation of a user device. Logic may be used to perform the steps as described in the above embodiments of the invention.

According to another embodiment of the invention, a network node is provided comprising a system as described above.

According to yet another embodiment of the present invention, there is provided an improved Serving Mobile Location Center (SMLC) including a system as described above.

According to the method and system as described above, an improvement to the Chan method is proposed, which can eliminate the limitations of the Chan method. Thus, better feasibility is provided in practical networks, especially in case of poor UE/BS geometry and poor TDOA measurements. Furthermore, a new solution based on "error metrics" to combine Chan and taylor methods is proposed and provides better robustness and accuracy compared to conventional solutions. On the other hand, the proposed solution is not computationally complex. Furthermore, the invention focuses only on the 2D case, but the idea can easily be extended to the 3D case by a person skilled in the art.

Drawings

The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, in which:

fig. 1 shows an OTDOA-based scenario for locating the position of a UE.

Fig. 2 shows simulation results of an example employing the Chan method.

Fig. 3 shows simulation results for another example with a non-peripheral base station employing the Chan method.

Fig. 4A to 4C show a simplified positioning system architecture according to the present application.

FIGS. 5A through 5C illustrate different positioning methods according to embodiments of the present invention;

fig. 6 shows simulation results of the Chan method and of the embodiment as proposed in fig. 4A and 5A; and

fig. 7 shows simulation results of the embodiment as proposed in fig. 4A and 5A and simulation results of the embodiment as proposed in fig. 4CA and 5C.

Corresponding reference characters indicate corresponding parts throughout the several views of the drawings.

Detailed Description

The following embodiments represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein.

To solve the above problems, the present invention provides a positioning system adapted to position or determine the position of a user equipment in a telecommunication system comprising at least three base stations, as shown in fig. 4A to 4C. Furthermore, as shown in fig. 5A to 5C, the present invention also provides a method for locating or determining the position of a user equipment in a telecommunication system comprising at least three base stations.

As shown in fig. 4A, 4B and 4C, the positioning system includes a first orientation estimation module 401 and a determination module 413. The first orientation estimation module 401 is configured to perform a first orientation estimation for the user equipment and to construct an initial estimated orientation of the user equipment. The determination module 413 is configured to determine a last estimated position of the user equipment.

As shown in fig. 4A, the determination module 413 comprises a calculation module 403, the calculation module 403 being configured to calculate a respective weighted difference for each of the initially estimated bearings obtained from the first bearing estimation module. The determination module 413 further comprises a decision unit 409, the decision unit 409 being configured to determine one of the first estimated positions having the smallest weighted difference as the last estimated position.

The system in fig. 4A operates as shown in fig. 5A. First, in step 501, the first orientation estimation module 401 performs first orientation estimation. It should be noted that the first azimuth estimation is here similar to the Chan method mentioned above. The first aspect estimation of the present invention mainly comprises:

a) obtaining a position (x, y) of a User Equipment (UE) and a position (x) of a first base station₁, y₁) And at least two adjacent base stations_i, y_i) TDOA-related measurements, wherein the first base station may be the base station serving the UE;

b) covariance matrix using TDOA measurementsDetermining a first estimate of a bearing of the UEAnd the distance r of the UE to the first base station₁(ii) an estimate of (d);

c) estimation of position using a UEAnd a distance r from the UE to the first base station₁Determining the distance r from the UE to at least two neighbouring base stations_iI =2 to M, where M is the number of measured base stations including the first base station and the at least two neighboring base stations;

d) covariance matrix using TDOA measurementsAnd distances r from the UE to at least two base stations_iI =2 to M, updating a first estimate of the position of the UEAnd a distance r from the UE to the first base station₁(ii) an estimate of (d);

e) updated first estimate of position using UEDetermining a position (x, y) at a User Equipment (UE) and a position (x) at a first base station₁,y₁) Square distance between them along x-axisAnd the square distance along the y-axis。

Specifically, the first orientation estimation is performed in step 501 as follows:

the first sub-step in step 501:

suppose thatIs an unknown vector, the estimator can thus be

Wherein

，

And is and

，

wherein,is the covariance matrix of the TDOA measurements,is a vectorM is the number of measured base stations, c is lightThe speed of the motor is controlled by the speed of the motor,andthe location of the ith base station and terminal, r_iIs the distance between the ith base station and the user equipment, an、、Respectively represent、、I =1 to M.

However,and, therefore,is unknown because it requires a true terminal orientation, an approximate estimator

Can then be neutralized in a first stepAndused together to obtain a preliminary estimateFrom which it is possible to calculateAn approximation of. Subsequently, equation (1) can be performed to estimate。

The second substep in step 501:

to further improve the position estimation, a second step is required.

Suppose thatIs an unknown vector. The estimator can thus be

Wherein

And is and

。

can pass through at the same timeVector defined by formula (1)In the middle useThe inverse substitution is performed to update equation (4).Defined by equations (2) and (3).

The first position estimation module then constructs four initial estimated positions in step 503 by:

；

；

(ii) a And is

，

Wherein,indicating an initial estimated orientation, an

Determined using an equation having the form：

If it is notThen, then；

If it is notThen, thenWherein z is₁(j) Is an unknown vector z₁The vector elements of (2).

To this end, four initial estimated orientations have been obtained. Subsequently, the method proposed herein proceeds to step 509 a. In step 509a, the calculation module 403 calculates a weighted difference for each initial estimated orientation. The weighted difference for each initial estimated orientation may be calculated using the following equation:

，

or

Wherein:

is a TDOA measurement representing the time difference between the ith base station and the first base station;

c is the speed of light;

is the ith diagonal element in the covariance matrix of the TDOA measurements; and

abs (.) is an operator that computes absolute values; and

it is to estimate the bearing at the k-th initial positionDistance from the ith base station, where dist (A, B) is the operator that computes the 2D distance between A and B, and k refers to the kth initial estimated bearing.

Finally, the decision unit 409 performs step 509b and determines the first estimated position having the smallest weighted difference as the last estimated position.

Fig. 4B shows yet another embodiment of the present invention. In contrast to fig. 4A, the determination module 413 in fig. 4B further comprises a second position estimation module 411, the second position estimation module 411 being configured to perform a second position estimation for the user equipment. Fig. 5B illustrates a process of operation of the system shown in fig. 4B.

As shown in fig. 4B and 5B, steps 501 and 503 are similar to the steps shown in fig. 5A. Except that four initial estimated positions constructed based on the results of the first position estimation are fed into the second position estimation module 411. Subsequently, the second position estimation module 411 performs four second position estimates, each with a corresponding one of the constructed initial estimated positions as an initial guess position. The second orientation estimate employs a combination of taylor series linearization and an iterative weighted least squares algorithm.

Subsequently, the four further estimated positions calculated or constructed by the second position estimation module 411 are fed into the calculation module 403 for calculating a respective further weighted difference for each further estimated position calculated or constructed from the second position estimate. This is shown in step 509 c. The additional weighted difference can be calculated here by using the formula 18 or 19. Finally, in step 509b, the decision unit 409 determines one of the further estimated positions having the smallest further weighted difference as the last estimated position.

Fig. 4C and 5C show another embodiment of the present invention. In contrast to the embodiments shown in fig. 4B and 5B, the system shown in fig. 4C further comprises a selection module 407. The selection module 407 is adapted to select at least one of the initially estimated bearings having the smallest weighted difference of the weighted differences as the initial guess bearing for performing the second bearing estimation. In particular, the selection module 407 is configured to select the initial estimated position corresponding to the smallest weighted difference if the difference between the second smallest weighted difference and the smallest weighted difference is larger than a threshold value, or the quotient of the second smallest weighted difference divided by the smallest weighted difference is larger than another threshold value, and otherwise to select both the initial estimated position corresponding to the smallest weighted difference and another initial estimated position corresponding to the second smallest weighted difference.

The operation of the system in fig. 4C operates as shown in fig. 5C. First, steps 501 and 503 are performed similarly to fig. 4A and 4B. The difference occurs in the subsequent steps. As shown in fig. 5C, initial estimated orientations are constructed in step 503, and then calculation module 403 calculates weighted differences for all initial estimated orientations using equations 18 or 19. This is shown in step 511. Subsequently, the selection module 407 selects at least one of the initially estimated orientations having the smallest weighted difference of the weighted differences. The selection step 513 can be implemented by sorting and comparing the weighted differences. If the second smallest weighted difference of the weighted differences is greater than the product of the constant and the smallest weighted difference of the weighted differences, then the selection module 407 will select the bearing from the initial estimated bearings corresponding to the smallest weighted difference as the initial guessed bearing in the second bearing estimate, otherwise the selection module 407 will select one estimated bearing from the initial estimated bearings corresponding to the smallest weighted difference of the weighted differences and the other estimated bearing from the initial estimated bearings corresponding to the second smallest difference of the weighted differences as the initial guessed bearing in the second bearing estimate.

The selected estimated position will be further sent to the second position estimation module 411 for further position estimation. At the same time, these selected estimated orientations and their differences are also sent together to the decision unit 409 for later decision-making.

Subsequently, the second position estimation module 411 performs a second position estimation for the user equipment, as shown in step 515.

If the selection module 407 selects only the initial estimated position with the smallest weighted difference as the initial guessed position for the second position estimate. This is shown in step 517. The calculation module 403 calculates a further weighted difference for a further estimated position calculated from the second position estimate. The decision unit 409 compares the difference of the further position estimate calculated from the second position estimate with the difference of the previously selected position from the initially estimated position, which as mentioned above was the position selected in step 513 and sent to the decision unit 409.

If the selection module 407 selects the two initial estimated bearings with the smallest two differences as the initial guessed bearings for the second bearing estimate, the calculation module 403 calculates a further weighted difference for the two further estimated bearings calculated or constructed by the second bearing estimation module 411 from the second bearing estimate. Subsequently, as shown in step 521, the one bearing with the smallest weighted difference is determined as the last estimated bearing from the group comprising the previously selected bearing from the first estimated bearings and the further estimated bearings.

The above steps can be implemented as follows:

will be provided withAccording to its error metricSorting, e.g., ascending. Assume that the orientation and error metrics of the rank are:

subsequently, the flow is executed:

<start of>

If it is not

Use of As an initial guess for the orientation of method 1, and an orientation estimate is obtained, e.g., Tay;

error metrics for two available results are calculated: ；

selecting one result with the smallest error metric as an orientation estimate;

otherwise

Use of As an initial guess for method 1, and obtain an orientation estimate, e.g., Tay 1;

use of As an initial guess for the orientation of method 1, and obtain an orientation estimate, e.g., Tay 2;

error metrics for four available results are calculated: ；

selecting one result with the smallest error metric as an orientation estimate;

end up

<End up>

Where "Thresh" is an empirically determined constant or threshold. Thresh =100 is a good compromise between computational complexity and accuracy of the final estimated orientation. In this procedure, mental health checks also help to exclude unreasonable anchor points, i.e. estimate orientations. Methods for mental health checks, which can be Cell-based positioning or enhanced Cell-based positioning, such as Cell-based positioning and angle of arrival determination (Cell + AoA) results, are well known to those skilled in the art and will not be discussed further in this application.

These steps will be more apparent to those skilled in the art from the following detailed examples:

example (c):

for error metrics with ordering：

；

The Taylor method only needs to be performed once, i.e. usingAs an initial guess orientation for the taylor method. Since 0.1932 is significantly lower than 44.2392, therefore,has high reliability.

However, for another with ranked error metrics：

；

Since the first two metrics are apparently close, they need to be measured separatelyAndtwo independent taylor calculations are fed in. The two taylor method outputs will then be compared according to the error metric, and the best output will be selected as the last estimated orientation.

Fig. 6 shows simulation results of the prior art Chan method and of the embodiment as proposed in fig. 4A and 5A. From fig. 6, it is apparent that the simulation results of the present application provide more accurate estimates. In FIG. 6, the coordinates of the real orientation 615 are [8, -1], and the noise variance is 0.03. The light black region 615 is a simulation result according to the embodiment shown in fig. 4A and 5A; the dark black area 609 is a simulation result according to the prior art.

Fig. 7 shows simulation results of the proposed method in the embodiment shown in fig. 4A and 5A and simulation results of the embodiment shown in fig. 4C and 5C. In fig. 7, the coordinates of the true azimuth are [8, -1], and the noise variance is 0.03. The light black area 711 indicates the simulation result according to the embodiment shown in fig. 4A and 5A; dark black area 709 indicates simulation results according to the embodiment shown in fig. 4C and 5C.

The above-described method can be embodied in a computer-readable medium. The above system can be implemented in a network node. The above system can also be implemented in a modified Serving Mobile Location Center (SMLC).

Throughout the description and claims of this specification, the words "comprise", "comprising" and variations thereof mean "including but not limited to", and are not intended to (and do not) exclude other components, integers or steps.

Throughout the description and claims of this specification, the singular encompasses the plural unless the context clearly requires otherwise. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context clearly requires otherwise.

It will be understood that the foregoing description of the embodiments of the invention has been presented for purposes of illustration and description. This description is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Modifications and variations are possible in light of the above description or may be acquired from practicing the invention. The claims and their equivalents define the scope of the invention.

Claims (19)

1. A method for locating the position of a user equipment in a telecommunication system comprising at least three base stations, the method comprising the steps of:

performing (501) a first orientation estimation for the user equipment using a quadratic correction least squares algorithm;

constructing (503) an initial estimated position based on the result of the first position estimate; and

determining (509) a final estimated position of the user equipment,

wherein the determining (509) step comprises one of:

mode 1:

calculating a weighted difference for the initial estimated position (509a), and determining the initial estimated position with the smallest weighted difference as the final estimated position (509 b);

mode 2:

calculating (511) a weighted difference for the initial estimated orientations and selecting (513) at least one of the initial estimated orientations with the smallest weighted difference as an initial guess for performing (515) a second orientation estimation using a combination of Taylor series linearization and an iterative weighted least squares algorithm,

the mode 2 further includes:

if one of the initial estimated bearings with the smallest weighted difference is selected as an initial guess bearing for performing the second bearing estimate, calculating (517) a further weighted difference for a further estimated bearing calculated from the second bearing estimate, and determining (523) the bearing with the smallest weighted difference as the last estimated bearing from a group comprising a previously selected bearing from the initial estimated bearings and the further estimated bearing;

if more than one of the initially estimated bearings is selected as initial guessed bearings for performing the second bearing estimation, calculating (519) an additional weighted difference for an additional estimated bearing calculated from the second bearing estimation and determining (521) the bearing with the smallest weighted difference as the last estimated bearing from the group comprising the previously selected bearing from the initially estimated bearings and the additional estimated bearing; and

mode 3:

applying (505) all of the initial estimated orientations to perform the second orientation estimation using a combination of Taylor series linearization and the iterative weighted least squares algorithm, calculating a weighted difference (509c) for estimated orientations obtained from the second orientation estimation, and determining the estimated orientation with the smallest weighted difference as the last estimated orientation (509b),

wherein the step of calculating the weighted difference is performed by using an equation having the form:

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{abs\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}{\sqrt{Q_{i-1}}},$

or

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{{\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}^2}{Q_{i-1}}$

Wherein:

d_i，1is a TDOA measurement representing the time difference between the ith base station and the first base station;

c is the speed of light;

Q_iis the ith diagonal element in the covariance matrix of the TDOA measurements;

abs (.) is an operator that computes absolute values;

is the distance between the kth initial estimated position of the user equipment and the ith base station; and

m is the number of base stations measured.

2. The method as recited in claim 1, wherein the step of selecting (513) at least one of said initial estimated positions further comprises: selecting the initial estimated position corresponding to the smallest weighted difference if the difference between the second smallest weighted difference and the smallest weighted difference is greater than a threshold value, or if the quotient of the second smallest weighted difference divided by the smallest weighted difference is greater than another threshold value, otherwise selecting both the initial estimated position corresponding to the smallest weighted difference and the initial estimated position corresponding to the second smallest weighted difference.

3. The method of claim 1 or 2, wherein the first orientation estimation is performed by employing the following equation:

z＝(G^TΨ^-1G)^-1G^TΨ^-1h

where z is the estimated quantity of the signal,

Ψ＝c²BQB，

B＝diag{r₂，r₃，...，r_M}，

$G=\left[\begin{array}{c}{x}_{2,1},y_{2,1},r_{2,1}\\ x_{3,1},y_{3,1},r_{3,1}\\ \mathrm{.......}\\ x_{M,1},y_{M,1},r_{M,1}\end{array}\right],$

and is

$h=\frac{1}{2}\left[\begin{array}{c}{r^2}_{2,1}-(K_2-K_1)\\ {r^2}_{3,1}-(K_3-K_1)\\ \mathrm{......}\\ {r^2}_{M,1}-(K_M-K_1)\end{array}\right],$

Where Q is the covariance matrix of the TDOA measurements, K_i＝x_i ²+y_i ²Is a vector [ x_i，y_i]^TM is the number of base stations measured, c is the speed of light, (x)_i，y_i) And (x, y) are the orientations of the ith base station and the terminal, respectively, r_iIs the distance between the ith base station and the user equipment, and x_i，1、y_i，l、r_i，1Respectively represent x_i-x₁、y_i-y₁、r_i-r₁And i is 1 to M.

4. The method of claim 3, wherein the first orientation estimation is further performed by employing the following equation:

z₁＝(G₁ ^TΨ₁ ^-1G₁)^-1G₁ ^TΨ₁ ^-1h₁

wherein z is₁Is a further estimator of the amount of time,

$G_1=\left[\begin{array}{cc}1& 0\\ 0& 1\\ 1& 1\end{array}\right],$

h₁＝(z-[x₁，y₁，0]^T)²，

Ψ₁＝4B₁(G₀Ψ^-1G₀)^-1B₁and is and

$B_1=\left[\begin{array}{cc}\hat{x}-x_1& 0\\ 0& \hat{y}-y_1\end{array}\right],$

wherein G is₀Is obtained by usingAn intermediate matrix obtainable instead of (x, y) in the vector z, and whereinIs a preliminary estimate of (x, y).

5. The method of claim 4, wherein the initial estimated position is constructed by:

$z_{p1}=\left[\begin{array}{c}sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

$z_{p2}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

and is

$z_{p4}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ -sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right],$

Wherein z is_pk(k-1, 2, 3, 4) represents the initial estimated bearing, and

determining sqrt _ z using an equation having the form₁(j)：

If z is₁(j) < 0, (j ═ 1, 2), then

If z is₁(j) Not less than 0, (j is 1, 2), then

Wherein z is₁(j) Are the vector elements of the further estimator.

6. A positioning system adapted for positioning a position of a user equipment in a telecommunication system comprising at least three base stations, the system comprising:

a first orientation estimation module (401) adapted to perform a first orientation estimation for the user equipment using a quadratic correction least squares algorithm and to construct an initial estimated orientation of the user equipment based on the result of the first orientation estimation; and

a determining module (413) connected to the first orientation estimation module and adapted to determine a last estimated orientation of the user equipment,

wherein the determining module (413) comprises:

a calculation module (403) adapted to calculate a weighted difference for the initial estimated orientation; and

a decision unit (409) configured to determine the bearing with the smallest weighted difference from the initial estimated bearing as the final estimated bearing,

wherein the calculation module (403) is adapted to calculate the weighted difference for the initial estimated orientation using an equation of the form:

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{abs\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}{\sqrt{Q_{i-1}}},$

or

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{{\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}^2}{Q_{i-1}}$

Wherein:

d_i，1is a TDOA measurement representing the time difference between the ith base station and the first base station;

c is the speed of light;

Q_iis the ith diagonal element in the covariance matrix of the TDOA measurements;

abs (.) is an operator that computes absolute values;

is the distance between the kth initial estimated position of the user equipment and the ith base station; and

m is the number of base stations measured.

7. The system of claim 6, wherein the determination module (413) further comprises:

a second position estimation module (411) adapted to perform a second position estimation using a combination of Taylor series linearization and an iterative weighted least squares algorithm, and to calculate one or more further estimated positions based on the result of the second position estimation,

wherein the calculation module (403) is further adapted to calculate a further weighted difference for the one or more further estimated orientations;

and wherein the decision unit (409) is further adapted to determine the position with the smallest weighted difference as the last estimated position from the group comprising the previously selected position from the first estimated position and the further estimated position.

8. The system according to claim 7, wherein said determining module (413) further comprises a selecting module (407), said selecting module (407) being connected to said calculating module, said determining unit and said second position estimating module, adapted to select at least one of said initially estimated positions having the smallest weighted difference as an initial guess position for performing said second position estimation.

9. The system according to claim 8, wherein the selection module (407) is configured to select the initial estimated position corresponding to the smallest weighted difference if a difference between the second smallest weighted difference and the smallest weighted difference is larger than a threshold value, or if a quotient of the second smallest weighted difference divided by the smallest weighted difference is larger than another threshold value, otherwise to select both the initial estimated position corresponding to the smallest weighted difference and the initial estimated position corresponding to the second smallest weighted difference.

10. The system of any of claims 6 to 9, wherein the first orientation estimation is performed by employing the following equation:

z＝(G^TΨ^-1G)^-1G^TΨ^-1h

where z is the estimated quantity of the signal,

Ψ＝c²BQB，

B＝diag{r₂，r₃，...，r_M}，

$G=\left[\begin{array}{c}{x}_{2,1},y_{2,1},r_{2,1}\\ x_{3,1},y_{3,1},r_{3,1}\\ \mathrm{.......}\\ x_{M,1},y_{M,1},r_{M,1}\end{array}\right],$

and is

$h=\frac{1}{2}\left[\begin{array}{c}{r^2}_{2,1}-(K_2-K_1)\\ {r^2}_{3,1}-(K_3-K_1)\\ \mathrm{......}\\ {r^2}_{M,1}-(K_M-K_1)\end{array}\right],$

Where Q is the covariance matrix of the TDOA measurements, K_i＝x_i ²+y_i ²Is a vector [ x_i，y_i]^TM is the number of base stations measured, c is the speed of light, (x)_i，y_i) And (x, y) are the orientations of the ith base station and the terminal, respectively, r_iIs the distance between the ith base station and the user equipment, and x_i，l、y_i，1、r_i、1Respectively represent x_i-x₁、y_i-y₁、r_i-r₁And i is 1 to M.

11. The system of claim 10, wherein the first orientation estimation is further performed by employing the following equation:

z₁＝(G₁ ^TΨ₁ ^-1G₁)^-1G₁ ^TΨ₁ ^-1h₁

wherein z is₁Is a further estimator of the amount of time,

$G_1=\left[\begin{array}{cc}1& 0\\ 0& 1\\ 1& 1\end{array}\right],$

h₁＝(z-[x₁，y₁，0]^T)²，

Ψ₁＝4B₁(G₀Ψ^-1G₀)^-1B₁and is and

$B_1=\left[\begin{array}{cc}\hat{x}-x_1& 0\\ 0& \hat{y}-y_1\end{array}\right],$

wherein G is₀Is obtained by usingAn intermediate matrix obtainable instead of (x, y) in the vector z, and whereinIs a preliminary estimate of (x, y).

12. The system of claim 11, wherein the initial estimated position is constructed by:

$z_{p1}=\left[\begin{array}{c}sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

$z_{p2}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

and is

$z_{p4}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ -sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right],$

Wherein z is_pk(k-1, 2, 3, 4) represents the initial estimated bearing, and

determining sqrt _ z using an equation having the form₁(j)：

If z is₁(j) < 0, (j ═ 1, 2), then

If z is₁(j) Not less than 0, (j is 1, 2), then

Wherein z is₁(j) Are the vector elements of the further estimator.

13. An article of manufacture for locating a position of a user equipment in a telecommunications system including at least three base stations, the article of manufacture comprising:

means for performing (501) a first orientation estimation for the user equipment using a quadratic correction least squares algorithm;

means for constructing (503) an initial estimated position based on the result of the first position estimate; and

means for determining (509) a last estimated position of the user equipment,

wherein the means for determining (509) comprises one of the following group of means:

group 1:

-means for calculating a weighted difference (509a) for the initial estimated position, and-means for determining the initial estimated position with the smallest weighted difference as the final estimated position (509 b);

group 2:

means for calculating (511) weighted differences for the initial estimated orientations, and selecting (513) at least one of the initial estimated orientations with the smallest weighted difference as an initial guess for performing (515) a second orientation estimate using a combination of Taylor series linearization and an iterative weighted least squares algorithm,

wherein said group 2 further comprises:

means for calculating (517) a further weighted difference for a further estimated position calculated from the second position estimate if one of the initial estimated positions with the smallest weighted difference is selected as an initial guess position for performing the second position estimate, and determining (523) the position with the smallest weighted difference as the last estimated position from a group comprising a previously selected position from the initial estimated positions and the further estimated position;

means for calculating (519) a further weighted difference for a further estimated position calculated from the second position estimate if more than one of the initially estimated positions is selected as initial guessed positions for performing the second position estimate, and determining (521) the position with the smallest weighted difference as the last estimated position from a group comprising the previously selected position from the initially estimated positions and the further estimated position; and

group 3:

means for applying (505) all of the initial estimated orientations to perform the second orientation estimation using a combination of Taylor series linearization and the iterative weighted least squares algorithm, means for calculating a weighted difference (509c) for estimated orientations obtained from the second orientation estimation, and means for determining the estimated orientation with the smallest weighted difference as the last estimated orientation (509b),

wherein the product operation for calculating the weighted difference is to use an equation of the form:

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{abs\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}{\sqrt{Q_{i-1}}},$

or

$Err\left(k\right)=\underset{i=2}{\overset{M}{\&Sigma;}}\frac{{\left(\right({\hat{r}}_{ik}-{\hat{r}}_{ik})/c-d_{i,1})}^2}{Q_{i-1}}$

Wherein:

d_i，1is a TDOA measurement representing the time difference between the ith base station and the first base station;

c is the speed of light;

Q_iis the ith diagonal element in the covariance matrix of the TDOA measurements;

abs (.) is an operator that computes absolute values;

is the distance between the kth initial estimated position of the user equipment and the ith base station; and

m is the number of base stations measured.

14. The product of claim 13, wherein the means for selecting (513) at least one of the initial estimated orientations further comprises:

means for selecting the initial estimated position corresponding to the smallest weighted difference if the difference between the second smallest weighted difference and the smallest weighted difference is greater than a threshold, or if the quotient of the second smallest weighted difference divided by the smallest weighted difference is greater than another threshold, and otherwise selecting both the initial estimated position corresponding to the smallest weighted difference and the initial estimated position corresponding to the second smallest weighted difference.

15. The product of claim 13 or 14, wherein the first orientation estimation is performed by employing the following equation:

z＝(G^TΨ^-1G)^-1G^TΨ^-1h

where z is the estimated quantity of the signal,

Ψ＝c²BQB，

B＝diag{r₂，r₃，...，r_M}，

$G=\left[\begin{array}{c}{x}_{2,1},y_{2,1},r_{2,1}\\ x_{3,1},y_{3,1},r_{3,1}\\ \mathrm{.......}\\ x_{M,1},y_{M,1},r_{M,1}\end{array}\right],$

and is

$h=\frac{1}{2}\left[\begin{array}{c}{r^2}_{2,1}-(K_2-K_1)\\ {r^2}_{3,1}-(K_3-K_1)\\ \mathrm{......}\\ {r^2}_{M,1}-(K_M-K_1)\end{array}\right],$

Where Q is the covariance matrix of the TDOA measurements, K_i＝x_i ²+y_i ²Is a vector [ x_i，y_i]^TM is the number of base stations measured, c is the speed of light, (x)_i，y_i) And (x, y) are the orientations of the ith base station and the terminal, respectively, r_iIs the distance between the ith base station and the user equipment, and x_i，1、y_i，1、r_i，1Respectively represent x_i-x₁、y_i-y₁、r_i-r₁And i is 1 to M.

16. The product of claim 15, wherein the first orientation estimation is further performed by employing the following equation:

z₁＝(G₁ ^TΨ₁ ^-1G₁)^-1G₁ ^TΨ₁ ^-1h₁

wherein z is₁Is a further estimator of the amount of time,

$G_1=\left[\begin{array}{cc}1& 0\\ 0& 1\\ 1& 1\end{array}\right],$

h₁＝(z-[x₁，y₁，0]^T)²，

Ψ₁＝4B₁(G₀Ψ^-1G₀)^-1B₁and is and

$B_1=\left[\begin{array}{cc}\hat{x}-x_1& 0\\ 0& \hat{y}-y_1\end{array}\right],$

wherein G is₀Is obtained by usingAn intermediate matrix obtainable instead of (x, y) in the vector z, and whereinIs a preliminary estimate of (x, y).

17. The article of claim 16, wherein the initial estimated position is constructed by:

$z_{p1}=\left[\begin{array}{c}sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

$z_{p2}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right];$

and is

$z_{p4}=\left[\begin{array}{c}-sqrt\_z_1\left(1\right)\\ -sqrt\_z_1\left(2\right)\end{array}\right]+\left[\begin{array}{c}{x}_1\\ y_1\end{array}\right],$

Wherein z is_pk(k-1, 2, 3, 4) represents the initial estimated bearing, and

determining sqrt _ z using an equation having the form₁(j)：

If z is₁(j) < 0, (j ═ 1, 2), then

If z is₁(j) Not less than 0, (j is 1, 2), then

Wherein z is₁(j) Are the vector elements of the further estimator.

18. A network node comprising the system of any of claims 6 to 12.

19. An improved serving mobile location centre comprising a system as claimed in any one of claims 6 to 12.